American Journal of Applied Sciences

A Computer Aided Diagnosis System for Lung Cancer Detection&bsol;Using Support Vector Machine

M. Gomathi and P. Thangaraj

DOI : 10.3844/ajassp.2010.1532.1538

American Journal of Applied Sciences

Volume 7, Issue 12

Pages 1532-1538

Abstract

Problem statement: Computer Tomography (CT) has been considered as the most sensitive
imaging technique for early detection of lung cancer. Approach: On the other hand, there is a
requirement for automated methodology to make use of large amount of data obtained CT images.
Computer Aided Diagnosis (CAD) can be used efficiently for early detection of Lung Cancer. Results:
The usage of existing CAD system for early detection of lung cancer with the help of CT images has
been unsatisfactory because of its low sensitivity and False Positive Rates (FPR). This study presents a
CAD system which can automatically detect the lung cancer nodules with reduction in false positive
rates. In this study, different image processing techniques are applied initially in order to obtain the
lung region from the CT scan chest images. Then the segmentation is carried with the help of Fuzzy
Possibility C Mean (FPCM) clustering algorithm. Conclusion/Recommendations: Finally for
automatic detection of cancer nodules, Support Vector Machine (SVM) is used which helps in better
classification of cancer nodules. The experimentation is conducted for the proposed technique by 1000
CT images collected from the reputed hospital.